How to get data from fcs to txt

An .exe file from Earl F Glynn, works fine with Wine on Ubuntu. Easiest solution I found
so far. If you want to do more with the data try
R

Linear, logarithmic, logicle

The best way to display the data depends on how the data were generated. If the signal
was linearly amplified (as is usual for forward and side scatters) than linear scale is used.

If the signal was logarithmically amplified than default choice is logarithmic (log) scale.
However log scale can't display events around zero. To solve this problem several other
scales started to be used in flow cytometry (logicle, hyperlog, biexponential). They allow
transition from linear scaling around zero to log scaling at higher values. The big advantage is
that the shape of distribution of near-zero events can be visualised.

This approach has also its downside: as it is data driven it differs from dataset to
dataset and therefore comparisons can be difficult. If consistency is needed in absolute
positioning of gates for negative/low events, then parameters of transforming function must
be same across samples or log scale used. However relative gate positioning based on
where the true biological population is located might be preferable anyway and that means
logicle display again.

Comparison of log scale (left) and biexponential scale (right). On
biexponential scale the gaussian-like distribution of negative cells is very clear.
Also their median flourescence can be assesed much more accurately (close to
zero, compare to "negative" population on log scale).

Logicle-like transformtions:

biexponential (I like how DiVa displays data, but that seems to be industrial top secret)

logicle?

arcsinh (Seems to be main for flowCore & Co., but sometimes a bit wierd.)

Hyperlog

It seems, that DiVa is guessing the parameters differently from Parks, et al.??

Note: If the transformation parameters are based on particular dataset, then the transform is best for that data.
However this will vary across samples!

Pulse height, width, area

… record them all if you have the chance.

Cells passing through cytometer generate voltage pulses of characteristic shape
(gaussian for round shaped cells). The ratio of area under the curve (integral) of the
pulse to its width (✕ height) is different for single cells
(upper panel) and doublets (lower panel). It can serve for exclusion of doublets from
analysis.

FlowCAP?

Synthetic

three_popul - 6-dimensional data of three normaly distributed and
overlaping "populations". The "populations" are one after each other in the file, each consisting of 700 datapoints.
This dataset was used for algorithm comparison in the paper.

TODO: Need easy multidimensional (normal distributions of e.g. 25 parameters), big (100 000 events) and small (1000 events).
To show, that even if big overlap populations can be separated if enough dimensions. So e.g. show how 5, 10, 25 parameters
resolve populations.

TODO: Need difficult data, non-normal, tailed, skewed, (+/-bent), outliers, noise. Make it as a mixture of nice and "not-so-nice"
populations. Small, but at least 500 (?) events per populations.